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How to Draw an Owl and Build Effective ML Stacks

Posted Apr 12
# Organization and Processes
# Systems and Architecture
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SPEAKER
Sarah Cantazaro
Sarah Cantazaro
Sarah Cantazaro
General Partner @ Amplify Partners

Sarah Catanzaro is a General Partner at Amplify Partners, where she focuses on investing in and advising high potential startups building data and machine learning tools, platforms, and applications. Her investments at Amplify include startups like OctoML, Intervenn Biosciences, RunwayML, and Hex among others. Sarah also has several years of experience defining data strategy and leading data science teams at startups and in the defense/intelligence sector including through roles at Mattermark, Palantir, Cyveillance, and the Center for Advanced Defense Studies.

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Sarah Catanzaro is a General Partner at Amplify Partners, where she focuses on investing in and advising high potential startups building data and machine learning tools, platforms, and applications. Her investments at Amplify include startups like OctoML, Intervenn Biosciences, RunwayML, and Hex among others. Sarah also has several years of experience defining data strategy and leading data science teams at startups and in the defense/intelligence sector including through roles at Mattermark, Palantir, Cyveillance, and the Center for Advanced Defense Studies.

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SUMMARY

TheyÕre handing us an engine, transmission, breaks, and chassis and asking us to build a fast, safe, and reliable car,Ó a data scientist at a recently IPOÕed tech company opined, while describing the challenges he faces in delivering ML applications using existing tools and platforms. Although hundreds of new MLOps products have emerged in the past few years, data scientists and ML engineers are still struggling to develop, deploy, and maintain models and systems. In fact, iteration speeds for ML teams may be slowing! In this talk, Sarah Catanzaro, a General Partner at Amplify Partners, will discuss a dominant design for the ML stack, consider why this design inhibits effective model lifecycle management, and identify opportunities to resolve the key challenges that ML practitioners face.

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Posted Apr 12 | Views 27
# Organization and Processes